Modelling Shallow Groundwater Level Fluctuations in Very Flat Landscapes Based on Satellite Data and Machine Learning

Abstract

Groundwater level fluctuations play a critical role in shaping hydrological extremes in flat sedimentary landscapes, where shallow water table depth (WTD) and strong surface-subsurface connectivity modulate the impacts of floods and droughts. The Western Pampean Plain (Argentina) exemplifies these dynamics; however, accurate modeling is often hindered by the lack of continuous in situ monitoring. In this context, manual WTD measurements collected by local farmers represent an underexploited source of information for modeling. In this study, we developed a Random Forest framework integrating farmer-operated observations with climatic and satellite-derived data and evaluated its ability to reconstruct and predict WTD. We tested seven modeling strategies, integrating: (i) climatic variables (including effects up to 15 months); (ii) high-resolution satellite-derived Surface Water Cover Index (SWCI) from Landsat; and (iii) coarse-resolution Terrestrial Water Storage Anomalies (TWSA) from GRACE. The best-performing model integrated climatic variables and SWCI, yielding strong reconstruction (R2 = 0.861, RMSE = 0.266 m) and robust prediction (R2 = 0.752, RMSE = 0.344 m) performances under cross-validation and rolling-origin validation, respectively. Model interpretation revealed SWCI as the dominant predictor, reflecting the strong surface-subsurface connectivity that characterizes this environment. This study provides a practical framework that integrates farmer-operated groundwater monitoring with freely available satellite observations to support agricultural decision-making in flood and drought risk management across flat sedimentary landscapes.